Adaptive filter circuits which employ the least mean square learning algorithm have widespread applications, which are continually growing in number. These adaptive filters employ a number of circuit legs which sequentially multiply a time delayed input signal with an error signal, integrate the product, and multiply the integrated product with the input time delayed signal. The longer the time constant of the integrator circuit, the more desirable is the integrator because it can effectively integrate over longer time periods. Long integrator time constants are useful for adaptive filter circuits because they increase the range of applications for which the adaptive filter can be used. For example, the smallest notch filter bandwidth that can be achieved by an adaptive filter is often determined by the length of the time constant. Conventional integrator circuits, such as simple RC networks cannot produce sufficiently long time constants, generally equal to RC, for many applications because the physical size of the capacitors would necessarily be too large for integrated semiconductor chips; and it is difficult to fabricate high value resistors with conventional integrated circuit technology.
Additionally, many integrator circuits when used in integrated circuits have unacceptably poor high frequency response for many adaptive learning applications, further limiting the usefulness of these circuits. For example, the frequency of operation of switched capacitor integrators is limited by the need for high amplifier bandwidth to provide sufficient settling accuracy for sampled-data signal processing.